摘要
面对以用户生成内容为核心的社交媒体新情境,在线评论对消费者购买决策的影响日渐凸显。以异质产品为研究对象,使用Scrapy爬虫框架和Redis数据库采集亚马逊中国的产品评论作为研究样本,探究在线评论的属性特征对搜索型产品和体验型产品销量的影响方式,对于进一步发展网络口碑理论具有重要的研究价值。研究表明,对于搜索型产品销量而言,评论极端性和评论长度的影响更加明显;在体验型产品中,评论数量的影响更加明显。研究成果有助于消费者有效识别有用的评论特征以及帮助电商企业建立高效的在线评论系统。
In the new circumstances of social media focusing on user-generated content,the impact of online reviews on consumers' shopping decision beome increasingly explicit. In this paper,we use Scrapy crawler framework and Redis database to collect online reviews of Amazon China's products as samples to study the impact of attributes of online reviews on sales of heterogeneous products. The results show that the review extremity and review depth have a greater impact on sales of search products. The volume of online reviews has more significant effect on sales of experience products. Meanwhile,our findings are expected to help consumers to better understand the helpful features of online review and help e-commerce websites improve their online review systems.
引文
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(1)从现有研究成果来看,商品评分的分布不服从正态分布而是J型分布,难以真实反映消费者口碑的整体表现。因此,这里不将评论平均得分作为自变量考虑。